SylabUZ
Course name | Basics of Machine Learning |
Course ID | 11.3-WK-CSEEP-BML-S22 |
Faculty | Faculty of Exact and Natural Sciences |
Field of study | computer science and econometrics |
Education profile | academic |
Level of studies | First-cycle studies leading to Bachelor's degree |
Beginning semester | winter term 2023/2024 |
Semester | 5 |
ECTS credits to win | 5 |
Course type | optional |
Teaching language | english |
Author of syllabus |
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The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
Lecture | 30 | 2 | - | - | Credit with grade |
Laboratory | 30 | 2 | - | - | Credit with grade |
The aim of the course is to familiarize students with the basic machine learning algorithms that are currently widely used in the practical analysis of various types of data sets.
The final goal of the course is for the student to acquire the ability to choose appropriate machine learning methods depending on the practical problem posed. The ability to discover patterns and rules hidden in data. The use of machine learning methods as support in the business decision support process.
Additionally, real data analyzes will be carried out using R software, which is currently very popular among analysts. After this course, the student will be able to use specialized R libraries to solve specific problems using machine learning algorithms.
Knowledge of the basics of statistics and probability theory.
Lecture/Lab:
Lecture: traditional and problem-based.
Laboratory: solving research problems using machine learning algorithms using specialized R program libraries. Discussion. Teamwork.
Outcome description | Outcome symbols | Methods of verification | The class form |
Checking the degree of students' preparation and their activity both in the laboratory and during the lecture.
The grade for the laboratory will be based on the results from the colloquium and/or projects.
Modified by dr Ewa Synówka (last modification: 10-04-2024 20:23)